Toward designing highly conductive polymer electrolytes by machine learning assisted coarse-grained molecular dynamics
Solid polymer electrolytes (SPEs) are considered promising building blocks of next-
generation lithium-ion batteries due to their advantages in safety, cost, and flexibility …
generation lithium-ion batteries due to their advantages in safety, cost, and flexibility …
Hierarchical multiresolution design of bioinspired structural composites using progressive reinforcement learning
A new method using reinforcement learning for designing bioinspired composite materials is
proposed. While bioinspired design of materials is a promising avenue, the possible …
proposed. While bioinspired design of materials is a promising avenue, the possible …
Accelerated discovery of high-strength aluminum alloys by machine learning
J Li, Y Zhang, X Cao, Q Zeng, Y Zhuang… - Communications …, 2020 - nature.com
Aluminum alloys are attractive for a number of applications due to their high specific
strength, and developing new compositions is a major goal in the structural materials …
strength, and developing new compositions is a major goal in the structural materials …
Self-focusing virtual screening with active design space pruning
High-throughput virtual screening is an indispensable technique utilized in the discovery of
small molecules. In cases where the library of molecules is exceedingly large, the cost of an …
small molecules. In cases where the library of molecules is exceedingly large, the cost of an …
The impact of supervised learning methods in ultralarge high-throughput docking
CN Cavasotto, JI Di Filippo - Journal of Chemical Information and …, 2023 - ACS Publications
Structure-based virtual screening methods are, nowadays, one of the key pillars of
computational drug discovery. In recent years, a series of studies have reported docking …
computational drug discovery. In recent years, a series of studies have reported docking …
[HTML][HTML] Multi-objective Bayesian materials discovery: Application on the discovery of precipitation strengthened NiTi shape memory alloys through micromechanical …
In this study, a framework for the multi-objective materials discovery based on Bayesian
approaches is developed and demonstrated on the efficient discovery of precipitation …
approaches is developed and demonstrated on the efficient discovery of precipitation …
A latent variable approach to Gaussian process modeling with qualitative and quantitative factors
Computer simulations often involve both qualitative and numerical inputs. Existing Gaussian
process (GP) methods for handling this mainly assume a different response surface for each …
process (GP) methods for handling this mainly assume a different response surface for each …
Autonomous materials discovery driven by Gaussian process regression with inhomogeneous measurement noise and anisotropic kernels
A majority of experimental disciplines face the challenge of exploring large and high-
dimensional parameter spaces in search of new scientific discoveries. Materials science is …
dimensional parameter spaces in search of new scientific discoveries. Materials science is …
Accelerated search for BaTiO3-based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning
An outstanding challenge in the nascent field of materials informatics is to incorporate
materials knowledge in a robust Bayesian approach to guide the discovery of new materials …
materials knowledge in a robust Bayesian approach to guide the discovery of new materials …
Rapid Bayesian optimisation for synthesis of short polymer fiber materials
The discovery of processes for the synthesis of new materials involves many decisions
about process design, operation, and material properties. Experimentation is crucial but as …
about process design, operation, and material properties. Experimentation is crucial but as …